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DNA Microarray Experiments (dna + microarray_experiment)
Selected AbstractsDNA Microarray Experiments: Biological and Technological AspectsBIOMETRICS, Issue 4 2002Danh V. Nguyen Summary. DNA microarray technologies, such as cDNA and oligonucleotide microarrays, promise to revolutionize biological research and further our understanding of biological processes. Due to the complex nature and sheer amount of data produced from microarray experiments, biologists have sought the collaboration of experts in the analytical sciences, including statisticians, among others. However, the biological and technical intricacies of microarray experiments are not easily accessible to analytical experts. One aim for this review is to provide a bridge to some of the relevant biological and technical aspects involved in microarray experiments. While there is already a large literature on the broad applications of the technology, basic research on the technology itself and studies to understand process variation remain in their infancy. We emphasize the importance of basic research in DNA array technologies to improve the reliability of future experiments. [source] Developing transgenic arabidopsis plants to be metal-specific bioindicatorsENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 1 2003Beth A. Krizek Abstract Deoxyribonucleic acid (DNA) microarrays provide a means to assess genome-wide expression patterns after exposure of an organism to different xenobiotics. Potential uses for this technology include identification of unknown toxicants, assessment of toxicity of new compounds, and characterization of the cellular mechanisms of toxicant action. Here we describe another use of DNA microarrays in toxicant-specific gene discovery. Combining results from two DNA microarray experiments, we have identified genes from the model plant Arabidopsis thaliana that are induced in response to one but not other heavy metals. The promoters of these genes should be useful in developing metal-specific transgenic biomonitors. To test this idea, we have fused the promoter of one of the newly identified Ni-inducible genes (AHB1) to the ,-glucuronidase (GUS) reporter gene. Arabidopsis plants containing the AHB1::GUS transgene show reporter gene activity when they are grown on media containing Ni but not when grown on media containing Cd, Cu, Zn, or without added metals. Thus, this approach has resulted in the creation of a transgenic strain of Arabidopsis that can report on the presence and concentration of Ni in plant growth media. Such transgenic models can serve as cheap and efficient biomonitors of bioavailable heavy metal contamination in soils and sediments. [source] An adaptive empirical Bayesian thresholding procedure for analysing microarray experiments with replicationJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 3 2007Rebecca E. Walls Summary., A typical microarray experiment attempts to ascertain which genes display differential expression in different samples. We model the data by using a two-component mixture model and develop an empirical Bayesian thresholding procedure, which was originally introduced for thresholding wavelet coefficients, as an alternative to the existing methods for determining differential expression across thousands of genes. The method is built on sound theoretical properties and has easy computer implementation in the R statistical package. Furthermore, we consider improvements to the standard empirical Bayesian procedure when replication is present, to increase the robustness and reliability of the method. We provide an introduction to microarrays for those who are unfamilar with the field and the proposed procedure is demonstrated with applications to two-channel complementary DNA microarray experiments. [source] Differential analysis of DNA microarray gene expression dataMOLECULAR MICROBIOLOGY, Issue 4 2003G. Wesley Hatfield Summary Here, we review briefly the sources of experimental and biological variance that affect the interpretation of high-dimensional DNA microarray experiments. We discuss methods using a regularized t -test based on a Bayesian statistical framework that allow the identification of differentially regulated genes with a higher level of confidence than a simple t -test when only a few experimental replicates are available. We also describe a computational method for calculating the global false-positive and false-negative levels inherent in a DNA microarray data set. This method provides a probability of differential expression for each gene based on experiment-wide false-positive and -negative levels driven by experimental error and biological variance. [source] | |||||||||||||